class_docstr: Detection Head of `DDOD <https://arxiv.org/abs/2107.02963>`_.
  
  DDOD head decomposes conjunctions lying in most current one-stage
  detectors via label assignment disentanglement, spatial feature
  disentanglement, and pyramid supervision disentanglement.
  
  Args:
      num_classes (int): Number of categories excluding the
          background category.
      in_channels (int): Number of channels in the input feature map.
      stacked_convs (int): The number of stacked Conv. Defaults to 4.
      conv_cfg (:obj:`ConfigDict` or dict, optional): Config dict for
          convolution layer. Defaults to None.
      use_dcn (bool): Use dcn, Same as ATSS when False. Defaults to True.
      norm_cfg (:obj:`ConfigDict` or dict): Normal config of ddod head.
          Defaults to dict(type='GN', num_groups=32, requires_grad=True).
      loss_iou (:obj:`ConfigDict` or dict): Config of IoU loss. Defaults to
          dict(type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0).
docstr: Get the anchors of each scale level inside.
  
  Args:
      num_level_anchors (list[int]): Number of anchors of each
          scale level.
      inside_flags (Tensor): Multi level inside flags of the image,
          which are concatenated into a single tensor of
          shape (num_base_priors,).
  
  Returns:
      list[int]: Number of anchors of each scale level inside.
docstr_tok: ["Get","the","anchors","of","each","scale","level","inside",".","Args",":","num_level_anchors","(","list","[","int","]",")",":","Number","of","anchors","of","each","scale","level",".","inside_flags","(","Tensor",")",":","Multi","level","inside","flags","of","the","image",",","which","are","concatenated","into","a","single","tensor","of","shape","(","num_base_priors",",",")",".","Returns",":","list","[","int","]",":","Number","of","anchors","of","each","scale","level","inside","."]
code: def get_num_level_anchors_inside(
      self, num_level_anchors: List[int], inside_flags: Tensor
  ) -> List[int]:
      """Get the anchors of each scale level inside.
  
      Args:
          num_level_anchors (list[int]): Number of anchors of each
              scale level.
          inside_flags (Tensor): Multi level inside flags of the image,
              which are concatenated into a single tensor of
              shape (num_base_priors,).
  
      Returns:
          list[int]: Number of anchors of each scale level inside.
      """
      split_inside_flags = torch.split(inside_flags, num_level_anchors)
      num_level_anchors_inside = [
          int(flags.sum()) for flags in split_inside_flags
      ]
      return num_level_anchors_inside
  
imports: from typing import List, Optional, Sequence, Tuple
  import torch
  import torch.nn
  from mmcv.cnn import ConvModule, Scale
  from mmengine.model import bias_init_with_prob, normal_init
  from mmengine.structures import InstanceData
  from torch import Tensor
  from mmdet.registry import MODELS, TASK_UTILS
  from mmdet.structures.bbox import bbox_overlaps
  from mmdet.utils import ConfigType, InstanceList, OptConfigType, OptInstanceList, reduce_mean
  from ..task_modules.prior_generators import anchor_inside_flags
  from ..utils import images_to_levels, multi_apply, unmap
  from .anchor_head import AnchorHead
label_desc: Use image node_id 14 for calling the DDODHead obj's underlying member method code with example usage: obj.get_num_level_anchors_inside(num_level_anchors, inside_flags) and returns: num_level_anchors_inside